Pancasila Hidup dan Lestari

1. Ketuhanan Uang Maha Esa.

2. Kemanusiaan Uang Pandir dan Biadab.

3. Persatuan Uang Indonesia.

4. Kerakyatan Uang Dipimpin oleh Nikmat Kebiadaban, Dalam Permusyahwatan dan Percakilan.

5. Keadilan Uang bagi Seluruh Rakyat Indonesia.

Global Citizen

I was accidentally born in Indonesia. Each of us was born with predetermined conditions, including nationality, we could never choose or refuse.

If the place we grew up in proves nurturing, we grew to love it. I love Surabaya mostly for its people. Surabayans are egalitarian in a country still reeks of feudalism, loud, prone to swear expletives, and say exactly what is in their mind. We are the rude Javanese, as most Indonesians would say.

This love of land is exploited in patriotic propaganda by a government and its apparatus that want loyalty and obedience from its citizens. The government would go even further by using our love of food, football club–or whatever else–and even religion to appeal to our patriotic zeal.

But we know better. Since the food was never created by such government. Only a few determined creative citizens created them, mostly out of love in what they do. The football club is in it for the money and does not care about any propaganda that does not make one. And even the religion is imported from a faraway land. So, what is claimed as ours–ours alone–is nothing but an illusion since it is never mine in the first place.

I feel even more stupid if I believe in such propaganda by claiming that Surabayan food is the best in the world. Because it never is. Since I actually like Indian breakfast of paratha, sambar, and chole, Vietnamese pho, Canadian beer, and Alberta beef steak. I grow to love other places and people too.

I have grown to be a global citizen. I can imagine living and dying in another place other than Indonesia. This was how I felt when my family and I left Indonesia last year after working there for 1 year to return to Canada. 

The place I grew up in does not have the same feeling as I felt when I was a kid. Java is now dotted with cities choked in traffic jams, floods, and overpopulation. Its government is corrupt and very slow to improve the welfare and health of its people. Its elite and rich is crass and ostentatious.

I was heartbroken but felt incapable of making enough changes. Maybe next time, I said to myself, when my kids are grown up. You see, first love is hard to erase. But at least this time, I am the one who will choose.

Indonesia Perlu Belajar dari India

Sebulan terakhir ini saya berkesempatan ke kota Villahermosa, Mexico, dan empat kota di India (New Delhi, Dehra Dun, Mumbai, Ahmedabad) untuk urusan kerja. Lebih kurang saya bekerja di tiap negara selama seminggu dan melihat keseharian dengan berlari di jalanan.

1. Kualitas Kota

Saya menilai kualitas kota dari empat parameter:

(i) kejernihan air sungai,
(ii) kelayakan trotoar untuk pejalan kaki,
(iii) keindahan taman kota, dan
(iv) kesemrawutan jalan.

Dari keempat parameter ini, kota-kota di Jawa (Bandung, Jakarta, Solo, Surabaya) yang pernah saya tinggali berada di bawah kota-kota semacam yang saya kunjungi di Mexico dan India, kecuali Mumbai.

Villahermosa, Mexico. Kota Villahermosa, Mexico adalah kota terbesar ke-27 di Mexico dan bisa dibandingkan dengan Malang, Jawa Timur. Jalan dari airport ke kota Villahermosa lebih rapi dan tertata, walaupun lebih kurang sama lebarnya. Tidak ada pengendara sepeda motor atau mobil yang seenaknya sendiri. Kualitas udara Villahermosa lebih bagus dari Surabaya dan Malang. Tidak saya lihat asap hitam mengepul dari bus kota yang melintasi jalan-jalan utama Villahermosa. Trotoar untuk pejalan kaki berfungsi dengan baik. Danau yang terletak tengah kota tertata apik dan masyarakat bisa menikmati suasana asri sekitar danau. Taman-taman kota tertata lumayan dan masyarakat bebas menikmati.

Dehra Dun, India. Kota kecil ini terletak di kaki pegunungan Himalaya. Suasana hiruk pikuknya seperti di kota Wonosobo, tempat kita dulu berakhir tahun saat di Indonesia. Walaupun kecil, airport Dehra Dun tertata bagus dan terawat.

Ahmedabad, India. Kota ini terletak di negara bagian Gujarat, tempat kelahiran Mahatma Gandhi, dan mempunyai jalan raya lapang walaupun kotanya jauh lebih kecil dari Mumbai atau New Delhi. Airport Ahmedabad juga tertata bagus dan terawat.

New Delhi, India. Daerah sekitar India Gate mempunyai jalan-jalan luas dan trotoar lebar dan rendah sehingga sangat menyenangkan buat pejalan kaki dan pelari jalan seperti saya. Banyak anjing liar di mana-mana, yang mungkin membuat takut sebagian kita jika tidak terbiasa. Polusi udara jauh di bawah Jakarta. Saya masih ingat sulitnya bernafas saat lari dari Cawang ke Senen karena polusi udara dari knalpot mobil dan sepeda motor yang luar biasa padatnya. Kesulitan bernafas ini tidak saya rasakan saat di New Delhi.

Taman-taman New Delhi lebih asri dari di Jakarta. Banyak penduduk New Delhi yang merebahkan diri untuk tidur siang di taman-taman. Taman-taman Jakarta dipagari dan tidak seasri yang saya lihat di New Delhi. Ada daerah kumuh di New Delhi, demikian juga di Jakarta, tapi keunggulan New Delhi sebagai ibu kota negara terlihat nyata.

Mumbai, India. Sekilas pandang, Mumbai seperti Jakarta. Padat, pengap, dan kumuh. Dharavi – perkampungan kumuh (slum) di jantung kota Mumbai – tampak dari luar seperti daerah Jatinegara atau Tambora, Jakarta. Saya pernah masuk ke perkampungan kumuh di balik gedung-gedung mewah Jakarta, tapi tidak sempat masuk ke Dharavi.

Mumbai juga hiruk pikuk dengan pembangunan gedung bertingkat tinggi. Bangunan-bangunan yang ada tidak semegah di Jakarta. Tapi di sini justru menurut saya Jakarta terlihat sangat timpang karena kemegahan yang ada berjejeran dengan kemelaratan yang menyesak.

2. Kualitas Pemimpin

Kualitas penduduk India yang terdidik, yang memimpin pemerintahan dan perusahaan, terlihat lebih bagus dari golongan terdidik Indonesia karena 3 hal:

1. Penguasaan bahasa Inggris. Bahkan pelayan hotel sampai sopir taksi di New Delhi dan Mumbai bisa berbahasa Inggris. Tidak ada kecanggungan sama sekali saat karyawan dan pimpinan mereka mengutarakan pendapat mereka dalam bahasa Inggris. Semua diutarakan dengan baik dan lengkap.

2. Kemampuan teknik bagus dan tidak takut mengutarakan pendapat. Pegawai Indonesia lebih banyak menanyakan hal-hal umum dan kurang berani mengutarakan pendapat mereka. Ini berbeda sekali dengan yang saya alami selama seminggu di India. Mereka tidak langsung menanyakan biaya proyek, tapi mengulas detail masalah-masalah teknis dan terlihat mempunyai ilmu dasar sains dan matematika kuat.

3. Kesederhanaan tenaga terdidik India lebih bagus dari yang di Indonesia. Saya sempat terkaget melihat pimpinan perusahaan minyak India memakai mobil Honda Civic. Mana mau pimpinan Pertamina memakai Honda Civic? Pengusaha beraset milyaran dolar yang ditemui saat meeting di New Delhi mengendarai sendiri mobil listrik Reva buatan Mahindra. Mana mau pemilik perusahaan Indonesia beraset puluhan milyar rupiah - apalagi puluhan trilyun rupiah - menyetir sendiri mobil listrik?

Jeleknya kualitas pemimpin Indonesia dibandingkan kualitas pemimpin India ini tampak lebih menyolok dibandingkan kualitas lingkungan hidup kota. Pemimpin India lebih sederhana walau lebih pintar dan berani. Semangat swadeshi India membuat saya kagum karena tidak cuma di ucapan saja.

Indonesia paling tidak ketinggalan dua langkah dari India. Indonesia perlu belajar dari India.

Walk Slowly

I often remind myself to walk slowly. As I walked back to my office from gym today, I felt good. Meeting a couple of colleagues there and checking whether I am still fit. I met several students who were diligently exercising. Note to self: If they complain about difficult courses I teach, remind them that they need to work as hard as they do in the gym.

I even paid attention to textures of buildings I passed along the way. Grey concrete corners now greeted me. Even symmetric pavement stones did not forget me. Often silent tree branches and twigs waved me by. I felt the quiet campus atmosphere: lectures ended last week and students must be busy studying now for final exams. The –15 C cold air did not feel as cold. These in-the-moment sensations filled me. When I walked slowly.

I often wondered why I had to walk fast. As if I was the most important person in the world. As if. I hardly save any moment by walking fast. It is when I sit and think that count, not when I walk.

The moments between sitting are precious. I walk slowly to reconnect with those around me. To walk slowly is divine. It is graceful and beautiful.

Hubungan Kerja Satu Arah

Beberapa kali saya diundang perusahaan2 Canada untuk presentasi dan perhatikan ada perbedaan budaya antara perusahaan2 Canada dengan perusahaan2 Indonesia. Yang paling menyolok adalah budaya perusahaan Canada yang lebih terbuka dan kesungguhan karyawan untuk bekerja.

Budaya perusahaan yang tidak terbuka di Indonesia sering karena atasan atau pemilik perusahaan tidak berpendidikan memadai. Atasan yang seperti ini memilih jaim dengan tidak berkomunikasi terbuka dengan bawahan dan karyawannya. Bisa karena malu ketahuan tidak berpendidikan. Bisa karena kurang wawasan, jadi tidak tahu harus berkomunikasi bagaimana. Bisa karena untuk mengurangi permintaan kenaikan gaji.

Alur komunikasi kemudian berjalan satu arah – dari atas ke bawah saja – dan perusahaan tidak berkembang karena team work tersumbat. Bawahan dan karyawan akhirnya menunggu perintah saja tanpa ada inisiatif dari bawah. Gaya bekerja ini lalu jadi budaya perusahaan.

Rantai pemutus komunikasi satu arah ada di tangan atasan atau pemilik perusahaan. Satu alasan yang sering diucapkan untuk tetap berkomunikasi satu arah adalah kualitas karyawan jelek, jadi tidak ada gunanya berkomunikasi dua arah. Kualitas karyawan jelek juga jadi alasan memberi gaji rendah. Jika karyawan merasa dua hal ini: gajinya rendah dan kualitasnya jelek, tapi tetap bekerja di perusahaan tersebut, maka sudah jodohnya karyawan bekerja di perusahaan ini. They deserve each other, kata orang bule.

Perusahaan2 asing di Indonesia makanya lebih maju karena mereka berani menggaji karyawan lebih tinggi. Perusahaan2 ini mendapat kualitas karyawan bagus dan berani menuntut kinerja tinggi. Mereka juga tidak enggan memecat karyawan yang tidak pecus. 

Sebaliknya, perusahaan nasional yang menggaji rendah karyawan sering bermain petak umpet jika karyawannya meminta kenaikan gaji, karena sebenarnya tidak mau memecat karyawan bergaji rendah. 

Undang Undang Ketenagakerjaan Indonesia tapi juga tidak memihak pemilik perusahaan karena UU ini terlalu melindungi karyawan. Belum lagi dengan tumpang tindihnya peraturan ketenagakerjaan Indonesia dan oknum-oknum pemerintah yang memanfaatkan situasi kemelut hubungan kerja perusahaan. UU Ketenagakerjaan yang imbang semestinya memudahkan atasan untuk memecat karyawan yang tidak pecus.

Karena di Indonesia karyawan digaji rendah dan atasan beranggapan karyawan tidak pecus bekerja, maka yang terjadi adalah iklim kerja petak umpet. Karyawan langsung menyebar resume untuk cari kerja baru begitu mulai bekerja satu minggu saja. Atasan langsung pasang muka jaim agar karyawan segan – kalau perlu takut – untuk meminta kenaikan gaji. 

Indonesia Butuh Partai Buruh

Perang kelas sosioekonomi sebenarnya berjalan sejak lama di Indonesia. Kaum buruh selalu jadi pecundang karena mereka kurang terorganisir, tidak berpendidikan, dan tidak berduit. Indonesia butuh Partai Buruh karena partai-partai sekarang berserikat kerap cuma berdasarkan kesamaan agama. Parpol-parpol berbau agama ini konsep usang dan tidak perlu dipertahankan jika mengingat apa yang barusan terjadi di pemilihan gubernur Jakarta dan pemilihan presiden AS. Konsep partai berbasis agama akan punah seiring bergeraknya jaman.

Pikirin ini timbul saat aku dan istri menikmati makan siang sushi buatannya. Dia nyeletuk, "Gila gak sih, jahe irisan buat makan sushi ini harganya Rp 100 ribu loh di Jakarta." Aku kontan aja bilang "Sementara pembantu rumah tangga cuma digaji Rp 500 ribu per bulan." "Heh, itu aja aku diprotes teman-temanku karena katanya merusak pasaran," selorohnya.

Merusak pasaran? "What the fuck!" seruku. Itu saja sama class warfare (perang kelas) karena kelompok yang kaya tidak mau membagi rejeki mereka dengan yang miskin (baca: kaum buruh), walaupun pembagian itu sebenarnya cukup fair. Yang kaya ingin mempertahankan status quo dengan cara berkomplot. Kerja pembantu sebulan dihargai 5 kaleng jahe teman makan sushi! Makan sekali di Pizza Hut di Tebet Square untuk 3 orang saja bisa kena Rp 250,000, yang sama dengan gaji 3 hari sopir Jakarta.

Servis yang diberikan kaum buruh di Indonesia amat sangat bagus. Tukang cukurku di Jatiwaringin bisa memplontos kepalaku dengan mulus berkilat. Di Calgary, tidak ada yang mau kerja seperti pak Agus – tukang cukurku di Jatiwaringin – jika aku tidak bayar Rp 300,000. Apalagi pekerjaan memplontos kepala mengkilat bukan pekerjaan mudah karena butuh tangan tangkas dan stabil seperti dokter bedah. Di Jakarta aku cukup bayar Rp 30,000 untuk cukur kepala plus pijat kepala.

Keterkaitan pendidikan dengan gaji rendah sangat jelas, tapi kaum buruh tidak mampu menikmati pendidikan bagus karena tidak punya uang. Kenapa tidak punya uang? Karena gaji mereka amat sangat rendah, diukur dengan cara apapun. Amat tidak masuk akal untuk bisa hidup dengan layak buat suami istri beranak 2 dengan gaji Rp 4 juta. Tapi sangat banyak keluarga Indonesia dengan gaji ini hidup di Jakarta dan punya anak lebih dari 2.

Kenapa aku bilang Rp 4 juta tidak cukup? Gampang menghitungnya. Makan 3 kali sehari untuk 4 orang butuh minimum Rp 50,000/hari jika masak sendiri. Sekolah 2 anak bisa memakan biaya Rp 300,000/bulan. Transportasi bisa Rp 20,000/hari, entah naik bus atau sepeda motor. Tiga biaya ini – makan, sekolah, transportasi – saja butuh Rp 2.4 juta. Belum biaya mencicil rumah dan sepeda motor. Belum biaya darurat untuk undangan kawin, rumah sakit, dan tabungan mudik. Belum lagi biaya pakaian dan tamasya ke kebun binatang Ragunan atau Ancol.

Baru beberapa gelintir pemimpin pemerintahan yang benar-benar ingin mengartikulasikan pemahaman tertindasnya kaum buruh ke aksi-aksi nyata. Salah satunya adalah Jokowi dan Ahok. Tapi Indonesia masih butuh banyak lagi untuk membela yang lemah. Indonesia butuh Partai Buruh!

Mechanics Problem 3: Sliding Pendulum

A small block of mass m sits at the left edge of a smooth hemispherical bowl of radius R and mass M. The small block is released from rest and slides up and down the bowl without friction, effectively moving as a pendulum. Assume as well that there is no friction between the ground and the the flat bottom of the bowl.

Determine the speed of the bowl when the small block reaches the bottom of the bowl. Determine the distance travelled by the bowl by the time the small block reaches the right edge of the bowl and stops there.

The Dirac-delta

The Dirac-delta functional δ(x–a) is an extremely useful mathematical object despite its abstract nature. It was proposed by Paul Dirac – an electrical engineer who decided to become a physicist and shared the 1933 Nobel Prize in Physics with Erwin Schrodinger. The story went that mathematicians did not like the way the Dirac-delta was derived and defined by its inventor, which was probably considered not rigorous enough, but in the end mathematicians recognized its beauty and importance. Paul Dirac also wrote a quantum mechanics textbook, The Principles of Quantum Mechanics, which was written concisely and beautifully at the same time.

The Dirac-delta exemplifies a proof that when an idea anchors several things at once, it needs not be explicit. It is a subtle object since it is defined by an integral representation:

∫ dx f(x)δ(x–a) = f(a),

where the integral is performed over an interval that includes x = a. δ(x–a) = 0 in almost everywhere except at x = a. Despite this extremely localized effect, it is so strong that it never gives a chance to f(x), as it is integrated over the interval, to yield anything other than its value at x = a. It is difficult to explicitly write what δ(x–a) is. There are several representations of the Dirac delta, e.g., a Gaussian peak and a Lorentzian peak, but they are still approximations to what the integral representation sufficiently captures.

Using the integral representation, we can derive three basic properties of the Dirac-delta:

1. ∫ dx f(x) δ⁽ⁿ⁾(x–a) = (-1)ⁿ f ⁽ⁿ⁾(a), where the (n) superscript indicates an nth derivative;

2. ∫ f(x) δ(ax–b) = a^-1 f(b/a);

3. ∫ dx f(x) δ(g(x)) = ∑ f(x_i)/g^'(x_i), where x_i are roots of g(x) and the ' superscript indicates a first derivative.

The marvel of the Dirac-delta is its ability to concisely sum up a complete set of continuous functions for the space of nonperiodic functions. It is used to represent a Fourier transform:

δ(x–y) = (2π)^-1 ∫ dk exp[ik(x–y)],

where the integral is over –∞ < k < ∞. This result is obtained from the Fourier series representation of the Dirac-delta and from extending the domain of the Fourier series representation to cover the entire space (–∞ < x < ∞).

The integral representation would become

∫ dx f(x) δ(x–y) = (2π)^-1 ∫ dx f(x) ∫ dk exp[ik(x–y)]

                          = (2π)^-1 ∫ dk exp[–iky] f(k) = f(y),

which defines both Fourier transform and its inverse of f(x). This is the most concise way to define a Fourier transform I know - as clearly explained by Mark Swanson's Path Integrals and Quantum Processes. A complete derivation of Fourier transform and its math details can found in Ian Sneddon's Fourier Transforms, which is a terrific source book on Fourier transform.

The Dirac-delta is used to model effects of periodic diffraction slits, to obtain Green's functions of various differential equations, and many more. It has wide-ranging applications, from specific mathematical models to fundamental equations.

Sirkus

Aku bukan diriku lagi

Lama tak ingat buat apa 

Teman kerabat mengejarku

Aku jadi orang mahapenting

Untuk masalah amat tak penting

Besok pakai baju apa

Nanti mau ngopi dimana
Kita beli gincu warna apa

Kemarin kamu difuck siapa

Mana peduli aku cuuk!

Saat aku tanya kamu cinta kenapa

Kamu bilang payudaraku elok

Tapi besok akan tertunduk layu

Kamu bilang mataku menyala

Tapi lusa redup terdera keriput

Mana peduli kamu cuuk!

Tak Sampai

Umur menapak senja, sebar senyumku

Tidak bisa sembunyikan hambar hatiku.

Hanya kau tahu: sembilu tersimpan lama

Walau aku sudah terima semua.

Hidupku terwujud palsu dan aku biasakan

Kubur mata hatiku dengan kealiman lazim.

Jalan sendiri aku di sisa hidup ini

Walau semua menyapaku: aku sepi.

Mechanics Problem 2

I like simple mechanics problems that illuminate fundamentals. An example is shown in the schematic diagram above. 

A small block of mass m₁ is released from rest at the tip of an inclined plane of a wedge of mass m₂. The wedge base has a length d and the incline has an angle θ. The small block slides on the incline and eventually reaches its bottom. The wedge will slide to the left as the small block slides.

Neglecting friction, determine the speed of the wedge when the small block reaches the bottom of the inclined plane.

Hint: This problem requires you to use conservation of momentum and conservation of energy, in addition to understanding inertial reference frame. An alternative way is to use Newton's second law and to use a clever kinematic constraint.

Beta Distribution

Out of the 5 probability distribution groups I discussed in my previous blog, one group I like the most is the beta distribution. It sits alone – like me when writing this blog – compared to the other 4 groups. Another group that stands alone is the hypergeometric distribution, but this can be approximated by binomial distribution at some point.

Beta distribution is still connected to binomial, but with a clever twist involving conditional probability. It is worth telling in this blog. The story goes like this.

Binomial distribution gives the probability of having r successes out of n trials, where the sequence of these r successes among the n trials does not matter. It is equal to n!/(r! (n–r)!) p^r(1-p)^n–r, and it is a conditional probability P(r | P = p) since it gives us the probability of getting r successes if there are n trials and the probability of success is p.

There are many situations where the value of p is unknown. For instance, if I work as a port authority's container inspection staff, I have to inspect thousands of incoming containers to the port. It is impossible to inspect all of these containers, so I have to sample only a fraction of them. As I sample them, I will find containers that have to be processed further and others that pass security and other checks. From the limited sampling I have done, I have to then determine what is the most likely total number of containers that actually need further processing. This data is useful if I would like to propose additional inspection staff, for example. In a situation like this, beta distribution becomes very useful.

Using example above, you can imagine that there are a lot of useful applications for beta distribution.

Beta distribution is obtained from the binomial using Bayes' theorem since it gives the conditional probability of probability, P(p | R = r), if there are r successes out of n trials from the sampling done. The Bayes' theorem states that P(p | R = r) P(r) = P(r | P = p) P(p). Since P(p) = 1 and P(r) = 1/(r + 1), we arrive at the beta distribution:

P(p | R = r) = (n+1)!/((n–r)! r!) p^r (1–p)^n–r.

It looks similar to the binomial distribution, but they are very different because p is the variable for beta distribution: it gives the probability distribution of probability of getting r successes out of n trials. In other words, to get a certain r successes for a given number of trials n, we can vary the probability p.

If the inspections are done many times, it is reasonable to seek the average of the probability p. But if the inspection is done once, it is the most likely probability p we need to find. Analyzing beta distribution further allows us to determine the optimum sampling size so that we can minimize errors and keep the inspection cost down.

The mean of p is equal to (r+1)/(n+2) with its attendant standard deviation. How about the most likely probability p? I will leave this for you to calculate.

5 Groups of 13 Probability Distributions

I teach a probability and statistics for engineers course this Fall term. One challenging task for students is to correctly pick the most appropriate probability distribution function for a particular problem. It requires them to think about its underlying probability structure and to precisely extract every bit of information from sentences of the problem. They learn that words "at least" or "given that" carry significant implications.

I cover standard discrete probability functions: (i) binomial, (ii) geometric, (iii) negative binomial, (iv) hypergeometric, (v) Poisson, and (vi) uniform, while for continuous probability functions: (vii) uniform, (viii) normal, (ix) exponential, (x) gamma, (xi) Weibull, (xii) lognormal, and (xiii) beta.

I am going to summarize these statistical distribution functions by simple illustrations to highlight the thought process that underlies the selection of the most appropriate distribution function.

1. The first group of the 13 distribution functions is the discrete uniform and the continuous uniform. They are picked if we know only the possible outcomes of a problem without a priori information on their individual likelihood. The outcomes are distinct from each other and are consequently thought to be independent of each other. In other words, a presence of one outcome does not influence the likelihood of another outcome.

We use the discrete uniform if we can count these outcomes as pure numbers (i.e., integers). For instance, a possible outcome of buying grocery is 2 apples. We use the continuous uniform if we deal with intervals, whether distance or time or something else. 12 km distance is an interval, thus a continuous variable. If we use the sign posts along the 12 km distance as counters, then we ought to use the discrete uniform.

2. The second group is headed by the binomial. We need this function to describe the probability of r successes occuring in x attempts. The binomial does not care the sequence of these r successes. For example, 3 successes and 1 failure can be described as p³(1–p), where p is the probability of a success. If we want 3 successes first and then followed by 1 failure – precisely in that order – then the expression p³(1–p) is equal to its probability. However, in many problems we do not care the sequence of appearance of the 3 successes. So, the sequence can be one of these 4: ppp(1–p), pp(1–p)p, p(1–p)pp, (1–p)ppp. Thus, there are 4 possible outcomes. The probability of having 3 successes and 1 failure in which ever order they appear is thus 4ppp(1–p).

Binomial function leads to Poisson function if the probability of success, p, is small. How small? It depends on the number of attempts x. Roughly, px should be several orders of magnitude smaller than x and x needs to be large (i.e., 1000 or more).

Binomial function leads to normal function if the number of attempts is large and our data are expressed in continuous variables, such as time duration or distance. The normal function requires that we have average and standard deviation information. The normal function can also be used – since it approximates binomial – to approximate binomial and Poisson function.

Lognormal function can be thought of as a variation of normal function. The probability of an outcome can be described by binomial function, but there is an exponential function relationship connecting the outcome probability and the binomial function. (Complicated, eh?)

3. The third group is headed by negative binomial. This group also describes the probability of r successes in x attempts. But we impose a peculiar sequence of appearance of the successes: we want 1 out of r successes to occur last in the x attempts. This means ppp(1–p) is ruled out as a possible outcome since the last attempt results in a failure of probability (1–p). Why do we need this peculiar sequence? This requirement describes "waiting time" problems. It describes the probability of waiting for a computer to malfunction, for example. It describes the probability of train arrival, and so on.

If we want 1 success only, the negative binomial function is reduced to the geometric function. From the negative binomial we can get the exponential function.

If our variable is continuous, then the negative binomial becomes the gamma distribution. (Gamma function is not appropriate name since it is referred to one well-known function.) Exponential function can be obtained from the gamma distribution when we want 1 success only.

The exponential function can also be derived from Poisson, so we have an overlap between the second and third group. This overlap occurs because we can describe possible outcomes of having at least 1 success using either Poisson (thus, binomial) or negative binomial function. Probability having at least 1 success is the opposite of probability of having no success. The description of a no-success outcome: (1–p)^x for x attempts is the same for either binomial or negative binomial.

Weibull function can be thought of as an improvement of exponential function since the latter describes a lifetime of a spare part that does not age. A spare part made of metals ages with time. Such spare part, such as a ball bearing, degrades over time and does not fail suddenly.

4. The fourth group is hypergeometric function. It describes the probability of sequentially picking r objects from a basket filled with x objects, given that this population of x objects is divided into two classes: "success" and "failure" objects. It is similar to binomial, but hypergeometric imposes a condition that p, the probability of success, is not constant: p depends on the remaining objects in the basket as we sequentially remove one object after another to yield a total of r objects.

It is expected, therefore, that if the number of objects in the basket is very large, then taking 1 object out, will not significantly change p. In this case, p can be considered constant if we do not take too many objects from the basket, i.e., r is much smaller than x. Hypergeometric function can thus be approximated by binomial when x is very large and r is much smaller.

5. The fifth group is beta function. It is related to binomial, but instead of concerning with the number of successes in a given number of attempts, we actually want to know what is the most likely probability of success given only information on the number of successes. The beta function – which is from the beta integral – tells us how we can guess the most fair probability based on incomplete information.

Jika Tuhan Tidak Ada

Jika Tuhan tidak ada hidup berarti waktu
Hanya sekali bukan singgah sebentar
Tak kenal dirimu waktumu hangus
Waktumu terbuang hidupmu lenyap

Jika Tuhan tidak ada semua hanya dongeng
Cerita lama masyarakat jaman baheula
Kamu pegang erat karena sungguh takut
Bayang akhir hidupmu sendiri

Jika Tuhan tidak ada kamu akan bingung
Biasa pikir timbang hari tunggu akhirat
Tiada larang berdalil dosa suruh janji pahala
Apa jadi pegangan hidup cuma sekali

Jika pun Tuhan ada belum tentu
Dia mau hidupkan kamu lagi
Terlalu bosan muak bermain sama
Pencium pantat tanpa mendebat

Morning Frost

Grass blade morning frost
Smooth supple bike tires

October zero degree sunrise
Helmet whirling calm wind

I sped my body arched
A simple pleasure I smiled

Simply

A floor not pillow suffices
warmth is from you the source

Good food my soul nourished
I more cannot ask for

Kids grow and leave me off
with me one grows old with

What I need I only take
often less more never

Probably

The probability of a probability of an event
occuring remains a probability that always
spans from zero to one, a space large enough
to accept minus infinity to plus infinity.
Where one might allude, insinuate or even
contradict if one has a correct number preceded
by words such as even though, provided that,
given that, who, and last but not least if.

It can be either A occurs given B occurs, or
the other way around, that is a probability
that B occurs given A occurs. And believe
me they are not the same. It can be either
the union or the intersection or the complement
or any combination thereof. And yes, counting
the number of events depending on permutations
and combinations almost always yields a huge
number that no one - almost always also - never
checks – as it is astronomical. So, forward your
arguments as tightly and cleanly as possible.

All of these are not nonsense, if you believe
in polls, weather reports, stock market, and
the odds your mom met your dad a long time
ago. And eventually marrying him. A lesson to
always think about the other side, the alternative,
the what-if, the what-not, the what-the-fuck.
As nothing is never trivial even the impossible.

Calgary

I live in a city at the foot of the Rockies, where the sky is
        the bluest of blue,
      June descends as hails the size of pebbles,
     winter lasts half a year despite chinook winds.
It taught me that west coast is my playground:
valleys and ridges with indelible names, winding roads punctuated by
   alpine lakes and campgrounds.
Where ever I go I miss my Yoho, Kananaskis, and Kootenay.

Rare it was to find candlenuts, shrimp chips, and red chillies,
yet they are now as easy as tzatziki, guacamole, mirin, and hummus.
They mingle, enhancing each other, so I sip tamarind soup with
soba noodles; bacon with red chillies could be next to try.
Its gastronomy expands, culture evolves. From cowboys of
           foothills and prairies to globe-reaching
petrochemical and energy exploration.

I was not from around here: a furthest point from my birthplace:
           a fate sealed by another luck.
I have grown to embrace this land:
       A huge mass cradling the Arctic, sheltered from perils of
global warming, political upheavals, international trade disputes.
A fortress cursed by luck of geography:
     northern edges of two vast oceans, impenetrable floe archipelago.

Octavio Paz's A Tree Within

A tree grew inside my head.
A tree grew in.
Its roots are veins,
its branches nerves,
thoughts its tangled foliage.
Your glance sets it on fire,
and its fruits of shade
are blood oranges
and pomegranates of flame.

Day breaks
in the body's night.
There, within, inside my head,
the tree speaks.

Come closer––can you hear it?

Why I Love Poem

A stomach a poem surely never fills

For it amply rewards the time food gives

I wager with you my dear true friend

Prettier a poem than the prettiest song

Picture is worth a thousand words say you

A thousand memories a poem is worth

It does not dance nor promulgate noises

With the quietest intent it softly speaks

It charts moments locations I write

For the deepest thoughts I have of you